text: import tensorflow as tf
  from tensorflow.python.framework import ops
  import os.path as osp
  
  base_dir = osp.dirname(osp.abspath(__file__))
  
  nn_distance_module = tf.load_op_library(
      osp.join(base_dir, 'tf_nndistance_so.so'))
  
  
  def nn_distance(xyz1, xyz2):
      '''
      Computes the distance of nearest neighbors for a pair of point clouds
      input: xyz1: (batch_size,#points_1,3)  the first point cloud
      input: xyz2: (batch_size,#points_2,3)  the second point cloud
      output: dist1: (batch_size,#point_1)   distance from first to second
      output: idx1:  (batch_size,#point_1)   nearest neighbor from first to second
      output: dist2: (batch_size,#point_2)   distance from second to first
      output: idx2:  (batch_size,#point_2)   nearest neighbor from second to first
      '''
      xyz1 = tf.expand_dims(xyz1, 0)
      xyz2 = tf.expand_dims(xyz2, 0)
      return nn_distance_module.nn_distance(xyz1, xyz2)
  
  # @tf.RegisterShape('NnDistance')
  @ops.RegisterShape('NnDistance')
  def _nn_distance_shape(op):
      shape1 = op.inputs[0].get_shape().with_rank(3)
      shape2 = op.inputs[1].get_shape().with_rank(3)
      return [tf.TensorShape([shape1.dims[0], shape1.dims[1]]), tf.TensorShape([shape1.dims[0], shape1.dims[1]]),
              tf.TensorShape([shape2.dims[0], shape2.dims[1]]), tf.TensorShape([shape2.dims[0], shape2.dims[1]])]
  
  
  @ops.RegisterGradient('NnDistance')
  def _nn_distance_grad(op, grad_dist1, grad_idx1, grad_dist2, grad_idx2):
      xyz1 = op.inputs[0]
      xyz2 = op.inputs[1]
      idx1 = op.outputs[1]
      idx2 = op.outputs[3]
      return nn_distance_module.nn_distance_grad(xyz1, xyz2, grad_dist1, idx1, grad_dist2, idx2)
  
  
  if __name__ == '__main__':
      import numpy as np
      import random
      import time
      #from tensorflow.python.kernel_tests.gradient_checker import compute_gradient
      from tensorflow.python.ops.gradient_checker import compute_gradient
      random.seed(100)
      np.random.seed(100)
      with tf.Session('') as sess:
          xyz1 = np.random.randn(32, 16384, 3).astype('float32')
          xyz2 = np.random.randn(32, 1024, 3).astype('float32')
          with tf.device('/gpu:0'):
              inp1 = tf.Variable(xyz1)
              inp2 = tf.constant(xyz2)
              reta, retb, retc, retd = nn_distance(inp1, inp2)
              loss = tf.reduce_sum(reta)+tf.reduce_sum(retc)
              train = tf.train.GradientDescentOptimizer(
                  learning_rate=0.05).minimize(loss)
          sess.run(tf.initialize_all_variables())
          t0 = time.time()
          t1 = t0
          best = 1e100
          for i in xrange(100):
              trainloss, _ = sess.run([loss, train])
              newt = time.time()
              best = min(best, newt-t1)
              print i, trainloss, (newt-t0)/(i+1), best
              t1 = newt
          # print sess.run([inp1,retb,inp2,retd])
          # grads=compute_gradient([inp1,inp2],[(16,32,3),(16,32,3)],loss,(1,),[xyz1,xyz2])
          # for i,j in grads:
              # print i.shape,j.shape,np.mean(np.abs(i-j)),np.mean(np.abs(i)),np.mean(np.abs(j))
          # for i in xrange(10):
              # t0=time.time()
              # a,b,c,d=sess.run([reta,retb,retc,retd],feed_dict={inp1:xyz1,inp2:xyz2})
              # print 'time',time.time()-t0
          # print a.shape,b.shape,c.shape,d.shape
          # print a.dtype,b.dtype,c.dtype,d.dtype
          # samples=np.array(random.sample(range(xyz2.shape[1]),100),dtype='int32')
          # dist1=((xyz1[:,samples,None,:]-xyz2[:,None,:,:])**2).sum(axis=-1).min(axis=-1)
          # idx1=((xyz1[:,samples,None,:]-xyz2[:,None,:,:])**2).sum(axis=-1).argmin(axis=-1)
          # print np.abs(dist1-a[:,samples]).max()
          # print np.abs(idx1-b[:,samples]).max()
          # dist2=((xyz2[:,samples,None,:]-xyz1[:,None,:,:])**2).sum(axis=-1).min(axis=-1)
          # idx2=((xyz2[:,samples,None,:]-xyz1[:,None,:,:])**2).sum(axis=-1).argmin(axis=-1)
          # print np.abs(dist2-c[:,samples]).max()
          # print np.abs(idx2-d[:,samples]).max()
  